Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations (1906.02367v2)

Abstract: Communication bottleneck has been identified as a significant issue in distributed optimization of large-scale learning models. Recently, several approaches to mitigate this problem have been proposed, including different forms of gradient compression or computing local models and mixing them iteratively. In this paper, we propose \emph{Qsparse-local-SGD} algorithm, which combines aggressive sparsification with quantization and local computation along with error compensation, by keeping track of the difference between the true and compressed gradients. We propose both synchronous and asynchronous implementations of \emph{Qsparse-local-SGD}. We analyze convergence for \emph{Qsparse-local-SGD} in the \emph{distributed} setting for smooth non-convex and convex objective functions. We demonstrate that \emph{Qsparse-local-SGD} converges at the same rate as vanilla distributed SGD for many important classes of sparsifiers and quantizers. We use \emph{Qsparse-local-SGD} to train ResNet-50 on ImageNet and show that it results in significant savings over the state-of-the-art, in the number of bits transmitted to reach target accuracy.

Overview of Qsparse-local-SGD: A Communication-Efficient Distributed Optimization Algorithm

The paper presented introduces the Qsparse-local-SGD algorithm, a novel method addressing the communication bottlenecks prevalent in distributed stochastic gradient descent (SGD) for training large-scale learning models. The innovative combination of quantization, sparsification, and local computation along with error compensation forms the crux of this methodology, presenting a significant stride towards communication-efficient distributed optimization.

The authors have adeptly pinpointed the communication bottleneck as a major hindrance in distributed learning scenarios, particularly when dealing with high-dimensional models over bandwidth-constrained networks. This impediment is increasingly relevant in architectures like federated learning, where model updates are aggregated from inherently distributed data sources such as edge devices.

Key Contributions and Methods

1. Algorithm Design: The Qsparse-local-SGD algorithm ingeniously fuses three primary techniques—quantization, sparsification, and local computations—to mitigate communication overhead. The algorithm maintains an error compensation mechanism that tracks discrepancies between true and compressed gradients, thus ensuring convergence stability.
2. Technical Approach:
   • Quantization: Through the use of stochastic quantizers such as QSGD, the algorithm reduces the precision of gradients, thereby lowering the volume of data transmitted across the network.
   • Sparsification: It employs sparsification strategies like Top_k and Rand_k, effectively transmitting only the most significant gradient components, and hence reducing communication costs.
   • Local Computation: By performing local updates and reducing the frequency of required synchronization across nodes, the algorithm significantly lessens the communication load.
3. Convergence Analysis: The authors present convergence guarantees for both synchronous and asynchronous implementations of Qsparse-local-SGD across smooth non-convex and strongly convex objectives. A pivotal finding is that the algorithm achieves convergence rates comparable to standard SGD, even with enhanced communication efficiency.
4. Experimental Evaluation: Implementation on the ResNet-50 using the ImageNet dataset showcases Qsparse-local-SGD's ability to reduce the communication budget by up to 15-20 times compared to classical methods, without degrading the model's accuracy or convergence speed.

Implications and Future Directions

The paper's findings open pathways for deploying distributed learning models in real-time constrained environments, such as IoT and mobile devices, where communication efficiency is paramount. The blend of quantization and sparsification adapted for each use case suggests versatility in varied application domains—ranging from edge AI to vast data-center computations.

In conjecturing future directions, an intriguing area is the exploration of adaptive techniques for dynamically adjusting quantization levels, sparsification rates, and local computation steps based on network conditions and computational capabilities. Moreover, expanding this framework to encompass more complex and heterogeneous models could yield further enhancements in distributed learning performance.

In essence, the Qsparse-local-SGD algorithm is a compelling advancement in communication-efficient distributed learning, robustly blending theoretical rigor with practical efficacy. The proposed methodologies and the accompanying convergence analyses mark a substantial contribution to overcoming the longstanding communication hurdles in distributed machine learning settings.